Tag: Large Language Model
All the articles with the tag "Large Language Model".
-
On the generalization of language models from in-context learning and finetuning: a controlled study
本文通过控制实验比较了语言模型在上下文学习和微调下的泛化能力,发现上下文学习更灵活,并提出通过数据增强方法显著改善微调的泛化性能。
-
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
The paper introduces 'Latte', a framework that transfers latent-level knowledge from Large Language Models during training to enhance few-shot tabular learning, outperforming baselines by leveraging unlabeled data and mitigating overfitting across diverse classification and regression tasks.
-
Large Language Model Compression with Global Rank and Sparsity Optimization
This paper introduces a two-stage LLM compression method using RPCA for low-rank and sparse decomposition and probabilistic pruning via policy gradient, outperforming state-of-the-art techniques at a 50% compression ratio while automatically adapting to layer-wise redundancy without manual thresholds or extensive fine-tuning.
-
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
This paper introduces Latent Preference Coding (LPC), a framework that uses discrete latent codes to model multifaceted human preferences, consistently improving the performance of offline alignment algorithms like DPO, SimPO, and IPO across multiple LLMs and benchmarks.
-
Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning
ARTIST, a novel framework unifying agentic reasoning, reinforcement learning, and tool integration, enables LLMs to autonomously orchestrate external tools within multi-turn reasoning, achieving up to 22% accuracy gains on complex math tasks and significant improvements in multi-turn function calling over baselines.